收费道路均衡配流的双层规划模型分析

考虑弹性用户需求下,当两点之间的收费额增加时,阻抗会随之增加,交通需求量会相应减少,引入新的交通阻抗函数。根据弹性交通需求下交通均衡配流,考虑一个双层规划模型来考虑道路经营者收费道路费率制定,同时在这个基础上给出了基于遗传一模拟退火的混合优化算法（GASA）求解算法,最后通过一个数值算例加以简单的论证,得到比较合理的费率。     

基于溢出交通需求的城市轨道交通线网规模测算模型

为了从道路建设的程度测算城市轨道交通线网的规模,分析了目前国内采用的轨道交通线网规模测算方法,提出了基于溢出交通需求的轨道交通线网规模测算模型。在保证规划年城市道路网能维持一定的服务水平的前提下,测算出城市交通总需求相对于道路供给的溢出量,将溢出交通需求换算成客运量转由轨道交通承担,利用轨道交通线网的负荷强度指标进而可确定轨道交通线网规模,并采用不同的方法对2020年西安市轨道交通线网规模进行了测算。测算结果表明:按出行需求推算的线网规模为103·68km,从财政实力"可能"的角度分析为66·32~94·74km,基于溢出交通需求推算为95·89km,计算结果基本一致,说明提出的测算模型可行。     

基于区域间投入产出的货运需求空间交流分析

货物运输空间交流即货物在起、讫点（OD）间的流量,既可以反映传统的运输统计中的货运发送量,也可以反映出货运到达量,同时还可以反映起点与讫点之间的交流关系,是空间运输联系的重要研究内容。长期以来,由于运输统计资料的不完备及OD调查费时耗力等原因形成该研究领域的瓶颈。运输是一种派生需求,空间运输联系是空间经济联系的一个表征,从空间经济联系入手,可以较全面地揭示空间运输联系的内在机理。《区域间投入产出表》较系统、全面地反映8大经济区域之间和部门之间的经济联系,利用该表深入到产业部门,由区域间产业空间交流揭示区域间货物运输的空间交流规律。     

考虑异质性出行需求的主支线公交树网络优化设计

公交线网优化设计是指在一定的运行约束条件下,选择1组公交线路和相关频率以达到优化目标的设计过程,可以表示为一个优化问题。针对具有高异质性出行需求的主支线公交树网络,在考虑客流需求和运营约束的前提下,以用户和运营者的成本最小为目标,提出了1种多目标非线性混合整数优化模型。优化变量为候选线路服务频率。为求解这一模型,设计了1种基于改进的布谷鸟算法的高效元启发式方法。该方法包括初始候选路线集生成过程;基于MNL模型的公交分配过程;确定路线服务频率的改进布谷鸟算法过程。通过算例验证了该方法的有效性和适用性。数值分析结果表明,该算法通过对所有可能的候选路径的服务频率选择得到接近最优的公交线路网络。另一方面,通过保持高峰时的公交线路为有效备择线路,为具有异质性出行需求的网络的重新设计提供了更好的解决方案。此外,该系统在1次运行中产生了1组帕累托解,其允许公交线网设计师评估运营商成本和乘客成本并做出折中方案。通过比较3种算法的计算结果和CPU时间,证明了改进的布谷鸟算法的可靠性和有效性。另外还研究了最优公交网络设计与公交运行速度、总需求规模等关键设计输入参数之间的关系,分析结果表明,关键设计输入参数与最优公交网络具有一定的协同效应。模型与算法为实际的大规模主支线公交树网络的优化设计提供了1种有效的工具。     

关于城市主干路设置辅路条件的研究

结合实际项目经验,通过分析道路功能定位、交通需求、交通组织方式及城市主干路辅路的设置条件等,根据所建立的计算模型,在设计规范的基础上对城市主干路设置辅路的可能性条件和必要性条件进行了深入研究,以期为类似设计研究提供参考。     

Machine learning applications in activity-travel behaviour research: a review

ABSTRACT

This paper reviews the activity-travel behaviour literature that employs Machine Learning (ML) techniques for empirical analysis and modelling. Machine Learning algorithms, which attempt to build intelligence utilizing the availability of large amounts of data, have emerged as powerful tools in the fields of pattern recognition and big data analysis. These techniques have been applied in activity-travel behaviour studies since the early ’90s when Artificial Neural Networks (ANN) were employed to model mode choice decisions. AMOS, an activity-based modelling system developed in the mid-’90s, has ANN at its core to model and predict individual responses to travel demand management measures. In the dawn of 2000, ALBATROSS, a comprehensive activity-based travel demand modelling system, was proposed by Arentze and Timmermans using Decision Trees. Since then researchers have been exploring ML techniques like Support Vector Machines (SVM), Decision Trees (DT), Neural Networks (NN), Bayes Classifiers, and more recently, Ensemble Learners to model and predict activity-travel behaviour. A large number of publications over the years and an upward trend in the number of published articles over time indicate that Machine Learning is a promising tool for activity-travel behaviour analysis and prediction. This article, first of its kind in the literature, reviews these studies and explores the trends in activity-travel behaviour research that apply ML techniques. The review finds that mode choice decisions have received wide attention in the literature on ML applications. It was observed that most of the studies identify the lack of interpretability as a serious shortcoming in ML techniques. However, very few studies have attempted to improve the interpretability of the models. Further, some studies report the importance of feature engineering in ML-based studies, but very few studies adopt feature engineering before model development. Spatiotemporal transferability of models is another issue that has received minimal attention in the literature. In the end, the paper discusses possible directions for future research in the area of activity-travel behaviour modelling using ML techniques.     

基于预约需求的共享停车平台泊位分配方法

为解决在预约需求下，考虑预约时刻、时长及延时需求的共享停车分配问题，提出一种共享停车泊位分配模型. 以平台收益和停车步行距离为优化目标，将需求分为基本及延时两种情况，确定停车预约请求的分配策略. 根据模型结构，设计随机解集生成方法，利用蒙特卡洛法确定模型的最优解. 以医院停车场及周边停车场为案例，测试模型. 结果表明，模型能较好地服务于共享停车泊位的分配，实现平台收益与满足需求之间的平衡.     

Stochastic dynamic switching in fixed and flexible transit services as market entry-exit real options

The first analytical stochastic and dynamic model for optimizing transit service switching is proposed for “smart transit” applications and for operating shared autonomous transit fleets. The model assumes a region that requires many-to-one last mile transit service either with fixed-route buses or flexible-route, on-demand buses. The demand density evolves continuously over time as an Ornstein-Uhlenbeck process. The optimal policy is determined by solving the switching problem as a market entry and exit real options model. Analysis using the model on a benchmark computational example illustrates the presence of a hysteresis effect, an indifference band that is sensitive to transportation system state and demand parameters, as well as the presence of switching thresholds that exhibit asymmetric sensitivities to transportation system conditions. The proposed policy is computationally compared in a 24-hour simulation to a “perfect information” set of decisions and a myopic policy that has been dominant in the flexible transit literature, with results that suggest the proposed policy can reduce by up to 72% of the excess cost in the myopic policy. Computational experiments of the “modular vehicle” policy demonstrate the existence of an option premium for having flexibility to switch between two vehicle sizes.     

The economic speed of an oceangoing vessel in a dynamic setting

The optimal (economic) speed of oceangoing vessels has become of increased importance due to the combined effect of low freight rates and volatile bunker prices. We examine the problem for vessels operating in the spot market in a tramp mode. In the case of known freight rates between origin destination combinations, a dynamic programming formulation can be applied to determine both the optimal speed and the optimal voyage sequence. Analogous results are derived for random freight rates of known distributions. In the case of independent rates the economic speed depends on fuel price and the expected freight rate, but is independent of the revenue of the particular voyage. For freight rates that depend on a state of the market Markovian random variable, economic speed depends on the market state as well, with increased speed corresponding to good states of the market. The dynamic programming equations in our models differ from those of Markovian decision processes so we develop modifications of standard solution methods, and apply them to small examples.